Supervised ImageNet

Supervised ImageNet research focuses on improving image classification models by leveraging the massive ImageNet dataset. Current efforts concentrate on enhancing data curation strategies, developing more efficient training methods (including exploring alternative architectures like binary neural networks and leveraging self-supervised learning), and addressing challenges like dataset bias and the need for explainable AI. These advancements are crucial for improving the accuracy, efficiency, and trustworthiness of computer vision systems across various applications, from medical imaging to agricultural technology.

Papers

November 7, 2023

Random Field Augmentations for Self-Supervised Representation Learning
Philip Andrew Mansfield, Arash Afkanpour, Warren Richard Morningstar, Karan Singhal
Supervised ImageNet Self Supervised Representation Learning Learned Representation Image Augmentation Random Field Random Augmentation

November 3, 2023

A Simple and Efficient Baseline for Data Attribution on Images
Vasu Singla, Pedro Sandoval-Segura, Micah Goldblum, Jonas Geiping, Tom Goldstein
Self Supervised Learning Supervised ImageNet Large Model Data Attribution Efficient Baseline

November 2, 2023

Scattering Vision Transformer: Spectral Mixing Matters
Badri N. Patro, Vijay Srinivas Agneeswaran
Vision Transformer Supervised ImageNet Vision Task Scattering Network Spectral Mixture

October 30, 2023

October 28, 2023

Benchmark Generation Framework with Customizable Distortions for Image Classifier Robustness
Soumyendu Sarkar, Ashwin Ramesh Babu, Sajad Mousavi, Zachariah Carmichael, Vineet Gundecha, Sahand Ghorbanpour, Ricardo Luna, Gutierrez Antonio Guillen, Avisek Naug
Supervised ImageNet Adversarial Sample Distortion Aware Image Classifier Image Classification Model Adversarial Benchmark

October 25, 2023

ConvNets Match Vision Transformers at Scale
Samuel L. Smith, Andrew Brock, Leonard Berrada, Soham De
Vision Transformer Supervised ImageNet Visual Analogue Scale High Performing 1D ConvNet nnU Net Performant Neural Network

October 24, 2023

October 23, 2023

SAMCLR: Contrastive pre-training on complex scenes using SAM for view sampling
Benjamin Missaoui, Chongbin Yuan
Computer Vision Supervised ImageNet Best View Contrastive Self Supervised Learning Complex Scene Contrastive Pre Training Elev Vision Sam Image Classification Datasets Similar Representation

October 22, 2023

A Pytorch Reproduction of Masked Generative Image Transformer
Victor Besnier, Mickael Chen
Supervised ImageNet Pytorch Model Bidirectional Transformer Masked Generative

October 16, 2023

Towards the Imagenets of ML4EDA
Animesh Basak Chowdhury, Shailja Thakur, Hammond Pearce, Ramesh Karri, Siddharth Garg
Supervised ImageNet Logic Synthesis Verilog Code Generation Hardware Platform Verilog Textbook

October 14, 2023

Does CLIP's Generalization Performance Mainly Stem from High Train-Test Similarity?
Prasanna Mayilvahanan, Thaddäus Wiedemer, Evgenia Rusak, Matthias Bethge, Wieland Brendel
Supervised ImageNet Generalization Performance Single CLIP Distribution Generalization Generalizable Representation STEM Education Train Test

October 13, 2023

Hypernymy Understanding Evaluation of Text-to-Image Models via WordNet Hierarchy
Anton Baryshnikov, Max Ryabinin
Supervised ImageNet Text to Image Model Text to Image Synthesis Chinese WordNet English WordNet

October 12, 2023

October 9, 2023

No Token Left Behind: Efficient Vision Transformer via Dynamic Token Idling
Xuwei Xu, Changlin Li, Yudong Chen, Xiaojun Chang, Jiajun Liu, Sen Wang
Vision Transformer Supervised ImageNet Computer Vision Task K TOKEN Image Token Efficient Vision Transformer Encrypted Vision Transformer

Supervised ImageNet

Papers

Random Field Augmentations for Self-Supervised Representation Learning

A Simple and Efficient Baseline for Data Attribution on Images

Scattering Vision Transformer: Spectral Mixing Matters

Addressing Weak Decision Boundaries in Image Classification by Leveraging Web Search and Generative Models

Are Natural Domain Foundation Models Useful for Medical Image Classification?

Maximum Knowledge Orthogonality Reconstruction with Gradients in Federated Learning

Benchmark Generation Framework with Customizable Distortions for Image Classifier Robustness

ConvNets Match Vision Transformers at Scale

Yin Yang Convolutional Nets: Image Manifold Extraction by the Analysis of Opposites

Cross-feature Contrastive Loss for Decentralized Deep Learning on Heterogeneous Data

SAMCLR: Contrastive pre-training on complex scenes using SAM for view sampling

A Pytorch Reproduction of Masked Generative Image Transformer

Towards the Imagenets of ML4EDA

Does CLIP's Generalization Performance Mainly Stem from High Train-Test Similarity?

Hypernymy Understanding Evaluation of Text-to-Image Models via WordNet Hierarchy

Is ImageNet worth 1 video? Learning strong image encoders from 1 long unlabelled video

SegLoc: Visual Self-supervised Learning Scheme for Dense Prediction Tasks of Security Inspection X-ray Images

CHIP: Contrastive Hierarchical Image Pretraining

XIMAGENET-12: An Explainable AI Benchmark Dataset for Model Robustness Evaluation

No Token Left Behind: Efficient Vision Transformer via Dynamic Token Idling